Pipeline versus Golden Entity Mention:
A large portion of prior work on Event Extraction has taken a simplified approach that only focused on ED and ARP, ignoring the training of EMD [1]}, [2]}, [3]}, [4]}, [5]}. Experiments for ED and ERP are ran based on golden annotation for entity mentions as input. Here in this work, we aim to have a more realistic approach where we start with the EMD sub-task and train other sub-tasks based on the entity mentions predicted by the EMD model. We explore various task setups, i.e. various combinations of single task, multi-task joint training, and sequential transfer learning to minimize the issue of error propagation inherent in the pipeline approach. Details of the various task setups are covered in Section REF .
